It’s 2014 and your teams are divided by platform, something like: Web, Mobile Web, Android, and iOS. In order to launch features, product managers jump from platform to platform and teams move fast. Really fast. Lines of code in each repository increase to the point where you now name them “monoliths.” A few engineers maintain these monoliths when they need to, but no one is solely dedicated to the task. Engineers are distributed by platform; so communication on when to maintain the monoliths is easy, but presents another problem. Can you continue to ship code efficiently if you depend entirely...

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Yelp extensively utilizes distributed batch processing for a diverse set of problems and workflows. Some examples include: Computation over Yelp’s review corpus to identify restaurants that have great views Training ML models to predict personalized business collections for individual users Analytics to extract the most in-demand service offerings for Request a Quote projects On-demand workloads to investigate surges in bot traffic so we can quickly react to keep Yelp safe Over the past two years, Yelp engineering has undertaken a series of projects to consolidate our batch processing technologies and standardize on Apache Spark. These projects aimed to simultaneously accelerate...

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Summary Here at Yelp, we generate business wins and a better platform by running A/B tests to measure the revenue impact of different user and business experience interventions. Accurately estimating key revenue indicators, such as the probability a customer retains at least \(n\)-days (\(n\)-day retention) or the expected dollar amount a customer spends over their first \(n\) days (\(n\)-day spend) is core to this experimentation process. Historically at Yelp, \(n\)-day customer or user retention was typically estimated as the proportion of customers/users we observed for more than \(n\) days who retained more than \(n\) days. Similarly, \(n\)-day spend was estimated...

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Last October we sent a group of Yelpers to the 2019 Grace Hopper Celebration! Here are a few takeaways and reflections from some of our attendees. Who attended? Surashree K., software engineer on Semantic Business Information Clara M., product design lead on Content Anna F., machine learning engineer on Semantic Business Information Nikunja G., software engineer on Infrastructure Security Catlyn K., software engineer on Stream Processing What was your favorite session? Surashree: Honestly, it’s hard to choose, but the one that stuck with me was the talk by Jackie Tsay and Matthew Dierker on Google’s Smart Compose, the Gmail feature...

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Varanus The monitor lizards are large lizards in the genus Varanus. Some time ago, our Android app got into a loop of sending data, due to some unlikely interactions between several different systems, which briefly overwhelmed our servers before we were able to turn it off. Fortunately, key code was behind an experiment. Otherwise, apps could have continued misbehaving for days, as there is no guarantee users would immediately update the app. It took an unusual combination of circumstances for this to happen, but this kind of problem seems to be a pervasive concern across the industry, and there are...

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Yelp’s mission is to connect users with great local businesses. As part of that mission, we provide local businesses with an ads product to help them better reach out to users. This product strives to showcase the most relevant ads to the user without taking away from their overall search experience on Yelp. In this blog post, we’ll walk through the architecture of how this is made possible by using one of the largest machine learning systems at Yelp: Ads Targeting System. The Ads Targeting System is a machine learning (ML) system designed to serve only the most relevant ads...

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We launched our very first Kafka cluster at Yelp more than five years ago. It was not monitored, did not expose any metrics, and we definitely did not have anyone on call for it. One year later, Kafka had already become one of the most important distributed systems running at Yelp, and today has become one of the core components of our infrastructure. Kafka has come a long way since the 0.8 version we were running back then, and our tooling (some of it open-source) has also significantly improved, increasing reliability and reducing the amount of operational work required to...

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Scaling security coverage in a growing company is hard. The only way to do this effectively is to empower front-line developers to be able to easily discover, triage, and fix vulnerabilities before they make it to production servers. Today, we’re excited to announce that we’ll be open-sourcing fuzz-lightyear: a testing framework we’ve developed to identify Insecure Direct Object Reference (IDOR) vulnerabilities through stateful Swagger fuzzing, tailored to support an enterprise, microservice architecture. This integrates with our Continuous Integration (CI) pipeline to provide consistent, automatic test coverage as web applications evolve. The Problem As a class of vulnerabilities, IDOR is arguably...

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